The 'Digital Twin' to enable the vision of precision cardiology

Abstract

Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twin' of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.

Keywords: Artificial intelligence; Computational modelling; Digital twin; Precision medicine.

Figure 1

The two pillars of the digital twin, mechanistic and statistical models, illustrating its construction and four examples of use: a1,  a2,  b1,  b2.

Figure 2

Conceptual summary of the main benefits of digital twin technologies.

Figure 3

Envisioned clinical workflow using the fully developed digital twin concept. Population data, collected from preceding patients and study cohorts, are used to create and validate statistical and mechanistic models, as well as to create a population-based digital twin (green). Novel patient data are analysed with the help of the existing models and integrated to form the patient’s digital twin (purple). The comparison and interaction between digital twins give valuable insight (phenotyping, risk assessment, prediction of disease development…) that is clinically interpreted and combined with traditional data to aid in the process of clinical decision-making. The digital twin develops in line with the patient’s condition—adjusting and improving in accordance with the follow-up data. Resulting outcomes are supplemented to shape population data and refine the follow-up data.

Figure 4

Synergy between mechanistic and statistical models in the definition of electrocardiogram (ECG) biomarkers for the management of hypertrophic cardiomyopathy.  ^,

Figure 5

The vision of a personalized in silico cardiology, where the digital twin informs all the stages through the clinical workflow. Models are used (i) to optimize data acquisition and the information extracted from it, (ii) to evaluate current health status and inform diagnosis and risk stratification, and (iii) to optimize clinical devices and drug selection to deliver a personalized therapy.

Take home figure

The cardiovascular digital twin that will deliver the vision of precision medicine by the synergetic combination of computer-enhanced induction (using statistical models learnt from data) and deduction (mechanistic modelling and simulation integrating multi-scale knowledge).